The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates
Abstract
We propose a space-time Markov random field (MRF) model to detect abnormal activities in video. The nodes in the MRF graph correspond to a grid of local regions in the video frames, and neighboring nodes in both space and time are associated with links. To learn normal patterns of activity at each local node, we capture the distribution of its typical optical flow with a mixture of probabilistic principal component analyzers. For any new optical flow patterns detected in incoming video clips, we use the learned model and MRF graph to compute a maximum a posteriori estimate of the degree of normality at each local node. Further, we show how to incrementally update the current model parameters as new video observations stream in, so that the model can efficiently adapt to visual context changes over a long period of time. Experimental results on surveillance videos show that our space-time MRF model robustly detects abnormal activities both in a local and global sense: not only does it accurately localize the atomic abnormal activities in a crowded video, but at the same time it captures the global-level abnormalities caused by irregular interactions between local activities.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Learning patterns of activity using real-time tracking
Stauffer, C., & Grimson, W. E. L. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868677 · Cited by 3,248
Incremental Learning for Robust Visual Tracking
Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0075-7 · Cited by 3,061
Unsupervised learning of finite mixture models
Figueiredo, M. A. T., & Jain, A. K. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.990138 · Cited by 2,125
Mixtures of Probabilistic Principal Component Analyzers
Tipping, M. E., & Bishop, C. M. (1999) · Neural Computation · doi:10.1162/089976699300016728 · Cited by 1,873
Robust online appearance models for visual tracking
Jepson, A. D., Fleet, D. J., & El-Maraghi, T. F. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1233903 · Cited by 922
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
Adam, A., Rivlin, E., Shimshoni, I., & Reinitz, D. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70825 · Cited by 918
Black, M. J., & Rangarajan, A. (1996) · International Journal of Computer Vision · doi:10.1007/bf00131148 · Cited by 757
A system for learning statistical motion patterns
Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., & Maybank, S. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.176 · Cited by 544
Detecting unusual activity in video
Zhong, H., Shi, J., & Visontai, M. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315249 · Cited by 495
Detecting Irregularities in Images and in Video
Boiman, O., & Irani, M. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-006-0009-9 · Cited by 439
Cited by
Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00179 · Cited by 1,930
Future Frame Prediction for Anomaly Detection - A New Baseline
Liu, W., Luo, W., Lian, D., & Gao, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00684 · Cited by 1,530
Anomaly detection in crowded scenes
Mahadevan, V., Li, W., Bhalodia, V., & Vasconcelos, N. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539872 · Cited by 1,512
Abnormal Event Detection at 150 FPS in MATLAB
Lu, C., Shi, J., & Jia, J. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.338 · Cited by 1,436
Anomaly Detection and Localization in Crowded Scenes
Li, W., Mahadevan, V., & Vasconcelos, N. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.111 · Cited by 1,017
Metadata
- Authors
- Kim, J., & Grauman, K.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 2921-2928
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2138092272
- Cited by
- 768
- References
- 14
- FWCI
- 12.93
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates. https://app.cochat.ai/papers/10.1109/cvpr.2009.5206569
- The paper
- Kim, J., & Grauman, K. (2009). Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates. 2009 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2009.5206569
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
